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(Optional) Optimization Problems
Sebastian Thrun,
Thad Starner,
and
Peter Norvig
Sebastian Thrun,
Thad Starner,
and
Peter Norvig
Learn about iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them. These techniques can often be used on intractable problems to find solutions that are "good enough" for practical purposes,...
Read more
Learn about iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them. These techniques can often be used on intractable problems to find solutions that are "good enough" for practical purposes,...
Read more
Learn about iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them. These techniques can often be used on intractable problems to find solutions that are "good enough" for practical purposes, and have been used extensively in fields like Operations Research & logistics. Finish the lesson by completing a classroom exercise comparing the different algorithms' performance on a variety of problems.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Thad Starner introduces the concept of _iterative improvement problems_, a class of optimization problems that can be solved with global optimization or local search techniques covered in this lesson.
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Syllabus
Thad Starner introduces the concept of _iterative improvement problems_, a class of optimization problems that can be solved with global optimization or local search techniques covered in this lesson.
Read more
Thad introduces _Hill Climbing_, a very simple local search optimization technique that works well on many iterative improvement problems.
Thad explains _Simulated Annealing_, a classical global optimization technique for optimization.
Thad introduces another optimization technique: _Genetic Algorithms_, which uses a population of samples to make iterative improvements towards the goal.
Complete a classroom exercise implementing simulated annealing to solve the traveling salesman problem.
Review similarities of the techniques introduced in this lesson with links to readings on advanced optimization topics, then complete an optimization exercise in the classroom.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops iterative improvement, hill climbing, simulated annealing, and genetic algorithms skills
Tailored for learners with backgrounds in operations research and logistics
Taught by renowned instructors with expertise in optimization algorithms
Involves hands-on exercises to reinforce learning
Suitable for learners interested in practical problem-solving techniques
Requires prior foundational knowledge in optimization
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(Optional) Optimization Problems.
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Career center
Learners who complete (Optional) Optimization Problems will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make predictions. They use a variety of techniques, including optimization, to analyze data and build models. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Data Scientists who need to find solutions that are "good enough" for practical purposes.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses advanced analytical techniques to solve complex problems in a variety of industries, including manufacturing, finance, and healthcare. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Operations Research Analysts who need to find solutions that are "good enough" for practical purposes.
Management Consultant
Management Consultant
Management Consultants help businesses improve their performance. They use a variety of techniques, including optimization, to analyze problems and develop solutions. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Management Consultants who need to find solutions that are "good enough" for practical purposes.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment decisions. They use a variety of techniques, including optimization, to analyze data and build models. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Financial Analysts who need to find solutions that are "good enough" for practical purposes.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They use a variety of techniques, including optimization, to improve the performance of their systems. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Software Engineers who need to find solutions that are "good enough" for practical purposes.
Statistician
Statistician
Statisticians use data to understand the world around us. They use a variety of techniques, including optimization, to analyze data and build models. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Statisticians who need to find solutions that are "good enough" for practical purposes.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They use a variety of techniques, including optimization, to build models and make predictions. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Quantitative Analysts who need to find solutions that are "good enough" for practical purposes.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They use a variety of techniques, including optimization, to build models and make predictions. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Actuaries who need to find solutions that are "good enough" for practical purposes.
Business Analyst
Business Analyst
Business Analysts use data to understand business problems and develop solutions. They use a variety of techniques, including optimization, to analyze data and build models. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Business Analysts who need to find solutions that are "good enough" for practical purposes.
Operations Manager
Operations Manager
Operations Managers oversee the day-to-day operations of a business. They use a variety of techniques, including optimization, to improve the efficiency and effectiveness of their operations. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Operations Managers who need to find solutions that are "good enough" for practical purposes.
Transportation Manager
Transportation Manager
Transportation Managers oversee the movement of people and goods. They use a variety of techniques, including optimization, to improve the efficiency and effectiveness of their transportation systems. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Transportation Managers who need to find solutions that are "good enough" for practical purposes.
Supply Chain Manager
Supply Chain Manager
Supply Chain Managers oversee the flow of goods and services from suppliers to customers. They use a variety of techniques, including optimization, to improve the efficiency and effectiveness of their supply chains. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Supply Chain Managers who need to find solutions that are "good enough" for practical purposes.
Logistics Manager
Logistics Manager
Logistics Managers oversee the movement of goods and services from suppliers to customers. They use a variety of techniques, including optimization, to improve the efficiency and effectiveness of their logistics operations. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Logistics Managers who need to find solutions that are "good enough" for practical purposes.
Project Manager
Project Manager
Project Managers plan and execute projects. They use a variety of techniques, including optimization, to manage resources and ensure that projects are completed on time and within budget. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Project Managers who need to find solutions that are "good enough" for practical purposes.
Warehouse Manager
Warehouse Manager
Warehouse Managers oversee the day-to-day operations of a warehouse. They use a variety of techniques, including optimization, to improve the efficiency and effectiveness of their operations. This course provides a strong foundation in optimization techniques, which are essential for success in this role. The course covers a variety of optimization algorithms, including gradient-free methods, which are often used to solve intractable problems. This knowledge will be invaluable to Warehouse Managers who need to find solutions that are "good enough" for practical purposes.
For more career information including salaries, visit:
OpenCourser.com/course/w2yjz8/optional
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
(Optional) Optimization Problems.
Provides intermediate and advanced mathematical optimization. It also provides a good foundation for understanding of optimization techniques.
Provides an intermediate and advanced theory behind optimization using linear algebra techniques to solve optimization problems.
Provides an intermediate and advanced theory behind optimization and provides some good case studies and background for using optimization.
Provides a good foundation for the theory behind convex optimization techniques and provides a good background for understanding some of the limitations behind optimization.
Provides a good selection of optimization problems and case studies.
Provides a wide range of case studies and methods for applied optimization.
Provides a good background for optimization algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/w2yjz8/optional
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Time
240 hours
Level
Advanced
Via
Udacity
Instructors
Sebastian Thrun
Thad Starner
Peter Norvig
Language
English
Good to know
Develops iterative improvement, hill climbing, simulated annealing, and genetic algorithms skills
Tailored for learners with backgrounds in operations research and logistics
Taught by renowned instructors with expertise in optimization algorithms
Involves hands-on exercises to reinforce learning
Suitable for learners interested in practical problem-solving techniques
Requires prior foundational knowledge in optimization
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